External validation of a chest radiograph-based prognostic model
Principal Investigator
Name
Jongmin Kim
Degrees
PH.D
Institution
MEDICAL IP
Position Title
Research & Science | Leader
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCOI-1242
Initial CDAS Request Approval
Jun 2, 2023
Title
External validation of a chest radiograph-based prognostic model
Summary
Chest radiographs contain diagnostic and prognostic information for various thoracic and cardiovascular diseases. The
current practice is limited to the qualitative analyses of radiographs by radiologists. It is reasonable to extract quantitative
data from chest radiographs automatically by using a deep learning model, which may be representative of thoracic and
cardiovascular abnormalities. Such information can be used for the prognostication and disease prevention. Our model,
which was developed outside the PLCO dataset, is able to capture prognostic imaging signatures from various anatomical
systems (e.g., lungs, heart, and vessels). We aims to externally validate our model using the PLCO dataset.
current practice is limited to the qualitative analyses of radiographs by radiologists. It is reasonable to extract quantitative
data from chest radiographs automatically by using a deep learning model, which may be representative of thoracic and
cardiovascular abnormalities. Such information can be used for the prognostication and disease prevention. Our model,
which was developed outside the PLCO dataset, is able to capture prognostic imaging signatures from various anatomical
systems (e.g., lungs, heart, and vessels). We aims to externally validate our model using the PLCO dataset.
Aims
The study purpose is to externally validate a chest radiograph-based, deep learning prognostic model, which can capture
prognsotic imaging signatures. The study outcomes include cardiovascular mortality and all-cause mortality.
Collaborators
Hyungjin Kim, Seoul National University Hospital